Self-organization of conceptual generalities and pattern-directed learning

Human expert decision makers can be characterized by their ability to perceive a hypothetical conceptual generality or pattern that is underlying a given collection of objects. The conventional cluster analysis is unable to generate such patterns since its clustering process is far from what the human experts actually do. That is, human experts form some concepts inductively from individual observations based on the conceptual “meaning” which the objects have. In this paper, by introducing an idea of prototype theory from a psychological domain with respect to human concept formation, an algorithm for human classification process is proposed. Based on this, the role of human generalization capability in his classification process is discussed with respect to the background semantic knowledge. The algorithm can be roughly divided into two phases; inductive prototype formation from training examples in a bottom-up fashion, and pattern-directed clustering of the instances being affected by the acquired concepts in a top-down fashion. Using a schematically-modelled example, the algorithm is illustrated with its implemented results. Our modelling method for the human classification process can be utilized for conceptual clustering that classifies a number of unknown objects into a distinguished group being affected by pre-acquired concepts.     

Self-reflective segmentation of human bodily motions using recurrent neural networks

For realizing a naturalistic collaboration between the human and the robot, we have to establish the intention sharing from the series of motion data that are observed and exchanged between the human and the machine. In a word, this is a problem to detect "meanings" out of the digitized data stream. In this paper, we propose a novel approach based on semiosis, and present a method of interpreting bodily motions using recurrent neural networks called Elman networks. We made some experiments using the raw data acquired while a human performs a simple task of fetching objects by stretching and folding his/her arm, and demonstrate that the network can learn invariant features of the generalized motion concepts, classify the motion by referring to self-organized memory structure, and understand a task structure of the observed human bodily motion. These capabilities are essential for machine intelligence to establishing the human-robot shared autonomy, a new style of human-machine collaboration proposed in the area of robotics.     

Bio-informatic activity modeling for human-artifacts symbiosis under resource boundedness

We investigate a biologically inspired design of an interface agent that is embedded inside human-artifact interactions rather than as an external observer, and has to work as an intelligent associate for a human user/operator in a time-critical situation like in an emergency. First, recent paradigmatic shifts of artifact design principles are discussed from an interdisciplinary viewpoint. Then, after the idea of Clancey’s activity modeling, we discuss the design principles of a situated interface agent. That is, different from the conventional supervisory agent’s task of seeking to optimize an isolated control task, such an agent has to be able to maintain its identity as an organism living within multiple contexts and looking inwards to consider the the nature of memory and perception, and looking outwards to consider the nature of social action with a human operator. Initially, our prior work using such a design principle is presented, and then decision-theoretic formulations of an interface agent’s activities are provided. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

Improvement of SH-wave EMAT phased array inspection by new eight segment probes

This paper presents a newly developed electromagnetic acoustic transducers (EMAT) probe with eight transmitter and eight receiver segments aiming to improve performance of a phased array EMAT probe with four transmitter and four receiver segments. Results of tests on artificial flaws in HAZ in a 6 inch diameter austenitic mock-up pipe were shown for characterizing its performance. The UT result with a conventional Piezo transducer was also referred for comparison.     

Dynamic version of steady state optimizing control of a distillation column by trial method

The best optimizing control procedure using an automatic optimizing controller, based on a simple pattern-search type, steepest ascent method is presented for a distillation column. The control system is composed of a conventional feedback loop by which the composition of overhead product is maintained constant, and an optimizing control unit which holds the partial derivatives of the objective function with respect to control variables to zero. The objective function used in this study is the profit rate obtained from the distillation process, and the two control variables; feed flow rate and energy supplied to the plant, are used.     

Statistical prediction of air pollution levels using non-physical models

This paper treats the prediction problem of air pollution levels at a short range by non-physical models. Main results are given as follows: (i) The prediction accuracy of the pollution levels by time series models is compared by evaluating three performance indices, and it is shown that the multiple linear regression model already proposed is better than the auto-regressive model, the Box-Jenkins' model and the persistence model. (ii) The multiple linear regression model is more improved if the model is classified by weather. (iii) The modeling accuracy is discussed for various sample sizes, and an appropriate sample size is determined from the experiment. (iv) The confidence intervals of the predicted means at a fixed time are calculated, and the combinations of the measurement times and the measured factors that improve the prediction accuracy are chosen. (v) A revised GMDH is proposed and the accuracy by this method is more improved than those by the time series models already presented. (vi) The Kalman filtering method is applied to the prediction of pollution levels, and the measured factors that improve the prediction accuracy are chosen.     

Functional safety analysis of SAE conditional driving automation in time-critical situations and proposals for its feasibility

The autonomous driving technology is rapidly developed for commercial use, aiming at the conditional driving automation or the third level of driving automation (LoDA 3). One of the most critical challenges for the achievement is the smooth authority transfer from the system to human drivers in an emergency. However, it is still on the way to find out fundamental solutions. The difficulty is closely related to the envisioned world problem, for which the concept of functional modeling could be a solution. This paper presents a safety analysis of the authority transfer problem based on the ideas of functional modeling. We discuss the safety of the authority transfer in a time-critical situation by using a simulator based on the functional resonance analysis method (FRAM). The result shows that the involvement of human drivers in driving activities is still essential even during autonomous driving. We show that the current LoDA 3 is a myth that the human drivers are supposed to be required just in non-normal situations, while they can be free from dynamic driving tasks (DDTs) in usual cases. Based on the result, this work makes some proposals for successful autonomous driving, effective human–machine collaboration, and the right design of artifacts.

     

Optimal estimation for continous system with jump process

The minimum variance estimator algorithm is derived for a class of linear continuous systems modulated by a multivalued jump Markov process. The approach adopted in this paper is as follows. First, we express the jump Markov process in terms of its initial value, the jump times and the values taken by the jump process after the jump, and then we apply the Bayes' rule and the general likelihood-ratio formula to obtain the a posteriori probability distribution of the jump process. The minimum variance estimate is given in terms of the a posteriori probability distribution of the jump process and the Kalman-filter estimates corresponding to the admissible values of the jump process. Simulation studies are also carried out to illustrate the behavior of the optimal estimator presented here.     

Measurement optimization with sensitivity criteria for distributed parameter systems

We consider the problem of optimally designing sensors for observation of a class of distributed parameter systems. The design of sensors concerns the choice of measurement conditions so that the information provided by measurements is maximal. This problem has been posed as a deterministic optimal control problem for a system equation of the Riccati type which governs a filter covariance. In the present study we introduce a functional called a sensitivity criterion by extending the Fisher information matrix to function spaces. It is shown that maximizing this criterion leads to a suboptimal solution of the sensor design problem associated with an infinite-dimensional state estimation problem. The existence theorem for a type of measurement control problem is proved and some numerical results are presented.     

Development of a 100keV electron beam nano lithography system

A wide range energy (25, 50, 100keV) electron beam lithography system with ZrO/W Schottky electron source and UHV chamber has been developed. The electron probe stability of 2.5%/hour is measured, and a beam diameter of 3nm is confirmed at 100keV beam energy. The ultimate pressure of bakeable work chamber is confirmed to reach 4×10⁻¹⁰Torr. With the UHV chamber and a gas jet nozzle, this system allows to perform in-situ electron beam nanolithography by combining with UHV multichamber systems.